Overview

Dataset statistics

Number of variables20
Number of observations3333
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory675.9 KiB
Average record size in memory207.6 B

Variable types

Categorical3
Numeric15
Boolean2

Alerts

State has a high cardinality: 51 distinct values High cardinality
Voicemailplan is highly correlated with NumbervmailmessagesHigh correlation
Numbervmailmessages is highly correlated with VoicemailplanHigh correlation
Totaldayminutes is highly correlated with TotaldaychargeHigh correlation
Totaldaycharge is highly correlated with TotaldayminutesHigh correlation
Totaleveminutes is highly correlated with TotalevechargeHigh correlation
Totalevecharge is highly correlated with TotaleveminutesHigh correlation
Totalnightminutes is highly correlated with TotalnightchargeHigh correlation
Totalnightcharge is highly correlated with TotalnightminutesHigh correlation
Totalintlminutes is highly correlated with TotalintlchargeHigh correlation
Totalintlcharge is highly correlated with TotalintlminutesHigh correlation
Numbervmailmessages has 2411 (72.3%) zeros Zeros
Customerservicecalls has 697 (20.9%) zeros Zeros

Reproduction

Analysis started2022-04-24 13:48:45.308702
Analysis finished2022-04-24 13:49:18.955483
Duration33.65 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

State
Categorical

HIGH CARDINALITY

Distinct51
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size181.1 KiB
WV
 
106
MN
 
84
NY
 
83
AL
 
80
WI
 
78
Other values (46)
2902 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6666
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKS
2nd rowOH
3rd rowNJ
4th rowOH
5th rowOK

Common Values

ValueCountFrequency (%)
WV106
 
3.2%
MN84
 
2.5%
NY83
 
2.5%
AL80
 
2.4%
WI78
 
2.3%
OH78
 
2.3%
OR78
 
2.3%
WY77
 
2.3%
VA77
 
2.3%
CT74
 
2.2%
Other values (41)2518
75.5%

Length

2022-04-24T10:49:19.226640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wv106
 
3.2%
mn84
 
2.5%
ny83
 
2.5%
al80
 
2.4%
wi78
 
2.3%
oh78
 
2.3%
or78
 
2.3%
wy77
 
2.3%
va77
 
2.3%
ct74
 
2.2%
Other values (41)2518
75.5%

Most occurring characters

ValueCountFrequency (%)
N734
 
11.0%
A687
 
10.3%
M612
 
9.2%
I515
 
7.7%
T412
 
6.2%
D380
 
5.7%
C356
 
5.3%
O346
 
5.2%
W327
 
4.9%
V322
 
4.8%
Other values (14)1975
29.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6666
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N734
 
11.0%
A687
 
10.3%
M612
 
9.2%
I515
 
7.7%
T412
 
6.2%
D380
 
5.7%
C356
 
5.3%
O346
 
5.2%
W327
 
4.9%
V322
 
4.8%
Other values (14)1975
29.6%

Most occurring scripts

ValueCountFrequency (%)
Latin6666
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N734
 
11.0%
A687
 
10.3%
M612
 
9.2%
I515
 
7.7%
T412
 
6.2%
D380
 
5.7%
C356
 
5.3%
O346
 
5.2%
W327
 
4.9%
V322
 
4.8%
Other values (14)1975
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII6666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N734
 
11.0%
A687
 
10.3%
M612
 
9.2%
I515
 
7.7%
T412
 
6.2%
D380
 
5.7%
C356
 
5.3%
O346
 
5.2%
W327
 
4.9%
V322
 
4.8%
Other values (14)1975
29.6%

Accountlength
Real number (ℝ≥0)

Distinct212
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.0648065
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:19.331983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q174
median101
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)53

Descriptive statistics

Standard deviation39.82210593
Coefficient of variation (CV)0.3940254508
Kurtosis-0.1078359806
Mean101.0648065
Median Absolute Deviation (MAD)27
Skewness0.09660629423
Sum336849
Variance1585.800121
MonotonicityNot monotonic
2022-04-24T10:49:19.468314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10543
 
1.3%
8742
 
1.3%
10140
 
1.2%
9340
 
1.2%
9039
 
1.2%
9538
 
1.1%
8638
 
1.1%
10037
 
1.1%
11637
 
1.1%
11236
 
1.1%
Other values (202)2943
88.3%
ValueCountFrequency (%)
18
0.2%
21
 
< 0.1%
35
0.2%
41
 
< 0.1%
51
 
< 0.1%
62
 
0.1%
72
 
0.1%
81
 
< 0.1%
93
 
0.1%
103
 
0.1%
ValueCountFrequency (%)
2431
 
< 0.1%
2321
 
< 0.1%
2252
0.1%
2242
0.1%
2211
 
< 0.1%
2172
0.1%
2151
 
< 0.1%
2122
0.1%
2102
0.1%
2093
0.1%

Areacode
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size181.1 KiB
415
1655 
510
840 
408
838 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9999
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row415
2nd row415
3rd row415
4th row408
5th row415

Common Values

ValueCountFrequency (%)
4151655
49.7%
510840
25.2%
408838
25.1%

Length

2022-04-24T10:49:19.587955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-24T10:49:19.652972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4151655
49.7%
510840
25.2%
408838
25.1%

Most occurring characters

ValueCountFrequency (%)
12495
25.0%
52495
25.0%
42493
24.9%
01678
16.8%
8838
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9999
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12495
25.0%
52495
25.0%
42493
24.9%
01678
16.8%
8838
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common9999
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12495
25.0%
52495
25.0%
42493
24.9%
01678
16.8%
8838
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII9999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12495
25.0%
52495
25.0%
42493
24.9%
01678
16.8%
8838
 
8.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size158.3 KiB
False
3010 
True
323 
ValueCountFrequency (%)
False3010
90.3%
True323
 
9.7%
2022-04-24T10:49:19.697758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Voicemailplan
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size158.3 KiB
False
2411 
True
922 
ValueCountFrequency (%)
False2411
72.3%
True922
 
27.7%
2022-04-24T10:49:19.727380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Numbervmailmessages
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.099009901
Minimum0
Maximum51
Zeros2411
Zeros (%)72.3%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:19.796982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile36
Maximum51
Range51
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.68836537
Coefficient of variation (CV)1.690128243
Kurtosis-0.05112853879
Mean8.099009901
Median Absolute Deviation (MAD)0
Skewness1.264823634
Sum26994
Variance187.3713466
MonotonicityNot monotonic
2022-04-24T10:49:19.925654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
02411
72.3%
3160
 
1.8%
2953
 
1.6%
2851
 
1.5%
3346
 
1.4%
2744
 
1.3%
3044
 
1.3%
2442
 
1.3%
2641
 
1.2%
3241
 
1.2%
Other values (36)500
 
15.0%
ValueCountFrequency (%)
02411
72.3%
41
 
< 0.1%
82
 
0.1%
92
 
0.1%
101
 
< 0.1%
112
 
0.1%
126
 
0.2%
134
 
0.1%
147
 
0.2%
159
 
0.3%
ValueCountFrequency (%)
511
 
< 0.1%
502
 
0.1%
491
 
< 0.1%
482
 
0.1%
473
 
0.1%
464
 
0.1%
456
 
0.2%
447
0.2%
439
0.3%
4215
0.5%

Totaldayminutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1667
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.7750975
Minimum0
Maximum350.8
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:20.056950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.92
Q1143.7
median179.4
Q3216.4
95-th percentile270.74
Maximum350.8
Range350.8
Interquartile range (IQR)72.7

Descriptive statistics

Standard deviation54.4673892
Coefficient of variation (CV)0.3029751615
Kurtosis-0.01994037885
Mean179.7750975
Median Absolute Deviation (MAD)36.3
Skewness-0.02907706714
Sum599190.4
Variance2966.696487
MonotonicityNot monotonic
2022-04-24T10:49:20.192887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1548
 
0.2%
159.58
 
0.2%
174.58
 
0.2%
183.47
 
0.2%
175.47
 
0.2%
162.37
 
0.2%
178.76
 
0.2%
194.86
 
0.2%
189.36
 
0.2%
146.36
 
0.2%
Other values (1657)3264
97.9%
ValueCountFrequency (%)
02
0.1%
2.61
< 0.1%
7.81
< 0.1%
7.91
< 0.1%
12.51
< 0.1%
17.61
< 0.1%
18.91
< 0.1%
19.51
< 0.1%
25.91
< 0.1%
271
< 0.1%
ValueCountFrequency (%)
350.81
< 0.1%
346.81
< 0.1%
345.31
< 0.1%
337.41
< 0.1%
335.51
< 0.1%
334.31
< 0.1%
332.91
< 0.1%
329.81
< 0.1%
328.11
< 0.1%
326.51
< 0.1%

Totaldaycalls
Real number (ℝ≥0)

Distinct119
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.4356436
Minimum0
Maximum165
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:20.333560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median101
Q3114
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.06908421
Coefficient of variation (CV)0.1998203376
Kurtosis0.2431815246
Mean100.4356436
Median Absolute Deviation (MAD)13
Skewness-0.111786639
Sum334752
Variance402.7681409
MonotonicityNot monotonic
2022-04-24T10:49:20.463991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10278
 
2.3%
10575
 
2.3%
9569
 
2.1%
10769
 
2.1%
10468
 
2.0%
10867
 
2.0%
9767
 
2.0%
10666
 
2.0%
11266
 
2.0%
11066
 
2.0%
Other values (109)2642
79.3%
ValueCountFrequency (%)
02
0.1%
301
 
< 0.1%
351
 
< 0.1%
361
 
< 0.1%
402
0.1%
422
0.1%
443
0.1%
453
0.1%
472
0.1%
483
0.1%
ValueCountFrequency (%)
1651
 
< 0.1%
1631
 
< 0.1%
1601
 
< 0.1%
1583
0.1%
1571
 
< 0.1%
1561
 
< 0.1%
1521
 
< 0.1%
1515
0.2%
1506
0.2%
1491
 
< 0.1%

Totaldaycharge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1667
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.56230723
Minimum0
Maximum59.64
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:20.594085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.288
Q124.43
median30.5
Q336.79
95-th percentile46.028
Maximum59.64
Range59.64
Interquartile range (IQR)12.36

Descriptive statistics

Standard deviation9.259434554
Coefficient of variation (CV)0.3029690947
Kurtosis-0.01981178724
Mean30.56230723
Median Absolute Deviation (MAD)6.17
Skewness-0.02908326834
Sum101864.17
Variance85.73712826
MonotonicityNot monotonic
2022-04-24T10:49:20.723156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.188
 
0.2%
27.128
 
0.2%
29.678
 
0.2%
31.187
 
0.2%
29.827
 
0.2%
27.597
 
0.2%
30.386
 
0.2%
33.126
 
0.2%
32.186
 
0.2%
24.876
 
0.2%
Other values (1657)3264
97.9%
ValueCountFrequency (%)
02
0.1%
0.441
< 0.1%
1.331
< 0.1%
1.341
< 0.1%
2.131
< 0.1%
2.991
< 0.1%
3.211
< 0.1%
3.321
< 0.1%
4.41
< 0.1%
4.591
< 0.1%
ValueCountFrequency (%)
59.641
< 0.1%
58.961
< 0.1%
58.71
< 0.1%
57.361
< 0.1%
57.041
< 0.1%
56.831
< 0.1%
56.591
< 0.1%
56.071
< 0.1%
55.781
< 0.1%
55.511
< 0.1%

Totaleveminutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1611
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.980348
Minimum0
Maximum363.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:20.865122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.8
Q1166.6
median201.4
Q3235.3
95-th percentile284.3
Maximum363.7
Range363.7
Interquartile range (IQR)68.7

Descriptive statistics

Standard deviation50.71384443
Coefficient of variation (CV)0.2523323545
Kurtosis0.02562975284
Mean200.980348
Median Absolute Deviation (MAD)34.4
Skewness-0.02387745608
Sum669867.5
Variance2571.894016
MonotonicityNot monotonic
2022-04-24T10:49:21.001271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.99
 
0.3%
167.27
 
0.2%
180.57
 
0.2%
2017
 
0.2%
161.77
 
0.2%
209.47
 
0.2%
230.97
 
0.2%
220.67
 
0.2%
195.57
 
0.2%
2306
 
0.2%
Other values (1601)3262
97.9%
ValueCountFrequency (%)
01
< 0.1%
31.21
< 0.1%
42.21
< 0.1%
42.51
< 0.1%
43.91
< 0.1%
48.11
< 0.1%
49.21
< 0.1%
52.91
< 0.1%
561
< 0.1%
58.61
< 0.1%
ValueCountFrequency (%)
363.71
< 0.1%
361.81
< 0.1%
354.21
< 0.1%
351.61
< 0.1%
350.91
< 0.1%
350.51
< 0.1%
348.51
< 0.1%
347.31
< 0.1%
341.31
< 0.1%
339.91
< 0.1%

Totalevecalls
Real number (ℝ≥0)

Distinct123
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.1143114
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:21.148779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.92262529
Coefficient of variation (CV)0.1989987746
Kurtosis0.206156468
Mean100.1143114
Median Absolute Deviation (MAD)13
Skewness-0.05556313904
Sum333681
Variance396.9109986
MonotonicityNot monotonic
2022-04-24T10:49:21.430666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10580
 
2.4%
9479
 
2.4%
10871
 
2.1%
10270
 
2.1%
9770
 
2.1%
8869
 
2.1%
10168
 
2.0%
10967
 
2.0%
9866
 
2.0%
11165
 
2.0%
Other values (113)2628
78.8%
ValueCountFrequency (%)
01
 
< 0.1%
121
 
< 0.1%
361
 
< 0.1%
371
 
< 0.1%
421
 
< 0.1%
431
 
< 0.1%
441
 
< 0.1%
451
 
< 0.1%
463
0.1%
486
0.2%
ValueCountFrequency (%)
1701
 
< 0.1%
1681
 
< 0.1%
1641
 
< 0.1%
1591
 
< 0.1%
1571
 
< 0.1%
1561
 
< 0.1%
1553
0.1%
1542
 
0.1%
1531
 
< 0.1%
1526
0.2%

Totalevecharge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1440
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.08354035
Minimum0
Maximum30.91
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:21.559889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.1
Q114.16
median17.12
Q320
95-th percentile24.17
Maximum30.91
Range30.91
Interquartile range (IQR)5.84

Descriptive statistics

Standard deviation4.310667643
Coefficient of variation (CV)0.2523287067
Kurtosis0.02548740481
Mean17.08354035
Median Absolute Deviation (MAD)2.92
Skewness-0.02385798901
Sum56939.44
Variance18.58185553
MonotonicityNot monotonic
2022-04-24T10:49:21.693381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.2511
 
0.3%
16.1211
 
0.3%
15.910
 
0.3%
17.099
 
0.3%
18.629
 
0.3%
17.999
 
0.3%
14.449
 
0.3%
18.968
 
0.2%
16.358
 
0.2%
16.978
 
0.2%
Other values (1430)3241
97.2%
ValueCountFrequency (%)
01
< 0.1%
2.651
< 0.1%
3.591
< 0.1%
3.611
< 0.1%
3.731
< 0.1%
4.091
< 0.1%
4.181
< 0.1%
4.51
< 0.1%
4.761
< 0.1%
4.981
< 0.1%
ValueCountFrequency (%)
30.911
< 0.1%
30.751
< 0.1%
30.111
< 0.1%
29.891
< 0.1%
29.831
< 0.1%
29.791
< 0.1%
29.621
< 0.1%
29.521
< 0.1%
29.011
< 0.1%
28.891
< 0.1%

Totalnightminutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1591
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.8720372
Minimum23.2
Maximum395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:21.832060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum23.2
5-th percentile118.18
Q1167
median201.2
Q3235.3
95-th percentile282.84
Maximum395
Range371.8
Interquartile range (IQR)68.3

Descriptive statistics

Standard deviation50.57384701
Coefficient of variation (CV)0.2517714646
Kurtosis0.08581607799
Mean200.8720372
Median Absolute Deviation (MAD)34.2
Skewness0.008921291065
Sum669506.5
Variance2557.714002
MonotonicityNot monotonic
2022-04-24T10:49:21.966447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191.48
 
0.2%
2108
 
0.2%
188.28
 
0.2%
197.48
 
0.2%
214.68
 
0.2%
193.67
 
0.2%
206.17
 
0.2%
194.37
 
0.2%
214.77
 
0.2%
231.57
 
0.2%
Other values (1581)3258
97.7%
ValueCountFrequency (%)
23.21
< 0.1%
43.71
< 0.1%
451
< 0.1%
47.41
< 0.1%
50.12
0.1%
53.31
< 0.1%
541
< 0.1%
54.51
< 0.1%
56.61
< 0.1%
57.51
< 0.1%
ValueCountFrequency (%)
3951
< 0.1%
381.91
< 0.1%
377.51
< 0.1%
367.71
< 0.1%
364.91
< 0.1%
364.31
< 0.1%
354.91
< 0.1%
352.51
< 0.1%
352.21
< 0.1%
350.21
< 0.1%

Totalnightcalls
Real number (ℝ≥0)

Distinct120
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.1077108
Minimum33
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:22.120254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile68
Q187
median100
Q3113
95-th percentile132
Maximum175
Range142
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.56860935
Coefficient of variation (CV)0.1954755452
Kurtosis-0.07201957894
Mean100.1077108
Median Absolute Deviation (MAD)13
Skewness0.03249957015
Sum333659
Variance382.9304717
MonotonicityNot monotonic
2022-04-24T10:49:22.255473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10584
 
2.5%
10478
 
2.3%
9176
 
2.3%
10272
 
2.2%
10069
 
2.1%
10669
 
2.1%
9867
 
2.0%
9466
 
2.0%
10365
 
2.0%
9564
 
1.9%
Other values (110)2623
78.7%
ValueCountFrequency (%)
331
< 0.1%
361
< 0.1%
381
< 0.1%
422
0.1%
441
< 0.1%
461
< 0.1%
481
< 0.1%
492
0.1%
502
0.1%
512
0.1%
ValueCountFrequency (%)
1751
 
< 0.1%
1661
 
< 0.1%
1641
 
< 0.1%
1581
 
< 0.1%
1572
0.1%
1562
0.1%
1552
0.1%
1542
0.1%
1533
0.1%
1523
0.1%

Totalnightcharge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct933
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.039324932
Minimum1.04
Maximum17.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:22.393407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile5.316
Q17.52
median9.05
Q310.59
95-th percentile12.73
Maximum17.77
Range16.73
Interquartile range (IQR)3.07

Descriptive statistics

Standard deviation2.275872838
Coefficient of variation (CV)0.2517746463
Kurtosis0.08566317984
Mean9.039324932
Median Absolute Deviation (MAD)1.54
Skewness0.008886236769
Sum30128.07
Variance5.179597173
MonotonicityNot monotonic
2022-04-24T10:49:22.525307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.6615
 
0.5%
9.4515
 
0.5%
8.4714
 
0.4%
8.8814
 
0.4%
7.6913
 
0.4%
8.6412
 
0.4%
10.811
 
0.3%
10.4911
 
0.3%
10.3511
 
0.3%
8.5711
 
0.3%
Other values (923)3206
96.2%
ValueCountFrequency (%)
1.041
< 0.1%
1.971
< 0.1%
2.031
< 0.1%
2.131
< 0.1%
2.252
0.1%
2.41
< 0.1%
2.431
< 0.1%
2.451
< 0.1%
2.551
< 0.1%
2.591
< 0.1%
ValueCountFrequency (%)
17.771
< 0.1%
17.191
< 0.1%
16.991
< 0.1%
16.551
< 0.1%
16.421
< 0.1%
16.391
< 0.1%
15.971
< 0.1%
15.861
< 0.1%
15.851
< 0.1%
15.761
< 0.1%

Totalintlminutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct162
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.23729373
Minimum0
Maximum20
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:22.677213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312.1
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation2.791839548
Coefficient of variation (CV)0.2727126546
Kurtosis0.6091847602
Mean10.23729373
Median Absolute Deviation (MAD)1.8
Skewness-0.2451359395
Sum34120.9
Variance7.794368064
MonotonicityNot monotonic
2022-04-24T10:49:22.826275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1062
 
1.9%
11.359
 
1.8%
9.856
 
1.7%
10.956
 
1.7%
10.153
 
1.6%
10.653
 
1.6%
10.253
 
1.6%
1152
 
1.6%
11.152
 
1.6%
9.751
 
1.5%
Other values (152)2786
83.6%
ValueCountFrequency (%)
018
0.5%
1.11
 
< 0.1%
1.31
 
< 0.1%
22
 
0.1%
2.12
 
0.1%
2.21
 
< 0.1%
2.41
 
< 0.1%
2.51
 
< 0.1%
2.61
 
< 0.1%
2.71
 
< 0.1%
ValueCountFrequency (%)
201
 
< 0.1%
18.91
 
< 0.1%
18.41
 
< 0.1%
18.31
 
< 0.1%
18.22
0.1%
183
0.1%
17.91
 
< 0.1%
17.82
0.1%
17.62
0.1%
17.53
0.1%

Totalintlcalls
Real number (ℝ≥0)

Distinct21
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.479447945
Minimum0
Maximum20
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:22.966271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.461214271
Coefficient of variation (CV)0.5494458917
Kurtosis3.083588982
Mean4.479447945
Median Absolute Deviation (MAD)1
Skewness1.321478166
Sum14930
Variance6.057575686
MonotonicityNot monotonic
2022-04-24T10:49:23.105856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3668
20.0%
4619
18.6%
2489
14.7%
5472
14.2%
6336
10.1%
7218
 
6.5%
1160
 
4.8%
8116
 
3.5%
9109
 
3.3%
1050
 
1.5%
Other values (11)96
 
2.9%
ValueCountFrequency (%)
018
 
0.5%
1160
 
4.8%
2489
14.7%
3668
20.0%
4619
18.6%
5472
14.2%
6336
10.1%
7218
 
6.5%
8116
 
3.5%
9109
 
3.3%
ValueCountFrequency (%)
201
 
< 0.1%
191
 
< 0.1%
183
 
0.1%
171
 
< 0.1%
162
 
0.1%
157
 
0.2%
146
 
0.2%
1314
0.4%
1215
0.5%
1128
0.8%

Totalintlcharge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct162
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.764581458
Minimum0
Maximum5.4
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:23.269005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.27
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)0.97

Descriptive statistics

Standard deviation0.7537726127
Coefficient of variation (CV)0.2726534284
Kurtosis0.6096104298
Mean2.764581458
Median Absolute Deviation (MAD)0.48
Skewness-0.2452865083
Sum9214.35
Variance0.5681731516
MonotonicityNot monotonic
2022-04-24T10:49:23.439669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.762
 
1.9%
3.0559
 
1.8%
2.6556
 
1.7%
2.9456
 
1.7%
2.7353
 
1.6%
2.8653
 
1.6%
2.7553
 
1.6%
2.9752
 
1.6%
352
 
1.6%
2.6251
 
1.5%
Other values (152)2786
83.6%
ValueCountFrequency (%)
018
0.5%
0.31
 
< 0.1%
0.351
 
< 0.1%
0.542
 
0.1%
0.572
 
0.1%
0.591
 
< 0.1%
0.651
 
< 0.1%
0.681
 
< 0.1%
0.71
 
< 0.1%
0.731
 
< 0.1%
ValueCountFrequency (%)
5.41
 
< 0.1%
5.11
 
< 0.1%
4.971
 
< 0.1%
4.941
 
< 0.1%
4.912
0.1%
4.863
0.1%
4.831
 
< 0.1%
4.812
0.1%
4.752
0.1%
4.733
0.1%

Customerservicecalls
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.562856286
Minimum0
Maximum9
Zeros697
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size181.1 KiB
2022-04-24T10:49:23.577123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.315491045
Coefficient of variation (CV)0.8417223368
Kurtosis1.730913655
Mean1.562856286
Median Absolute Deviation (MAD)1
Skewness1.091359482
Sum5209
Variance1.730516689
MonotonicityNot monotonic
2022-04-24T10:49:23.873145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11181
35.4%
2759
22.8%
0697
20.9%
3429
 
12.9%
4166
 
5.0%
566
 
2.0%
622
 
0.7%
79
 
0.3%
92
 
0.1%
82
 
0.1%
ValueCountFrequency (%)
0697
20.9%
11181
35.4%
2759
22.8%
3429
 
12.9%
4166
 
5.0%
566
 
2.0%
622
 
0.7%
79
 
0.3%
82
 
0.1%
92
 
0.1%
ValueCountFrequency (%)
92
 
0.1%
82
 
0.1%
79
 
0.3%
622
 
0.7%
566
 
2.0%
4166
 
5.0%
3429
 
12.9%
2759
22.8%
11181
35.4%
0697
20.9%

Churn
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size181.1 KiB
0
2850 
1
483 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3333
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Length

2022-04-24T10:49:24.068882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-24T10:49:24.135492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Most occurring characters

ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3333
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common3333
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Interactions

2022-04-24T10:49:16.537781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:49.021555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:50.912048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:52.789794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:54.820743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:56.690521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:58.416193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:00.437880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:02.418168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:04.261421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:06.451029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:08.400893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:10.419577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:12.558726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:14.532095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:16.656161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:49.195040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:51.032445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:52.914559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:54.930732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:56.799124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:58.529910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:00.547891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:02.524067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:04.386972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:06.574509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:08.531084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:10.538035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:12.709068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:14.650926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:16.765646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:49.331750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:51.136446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:53.046149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:55.105520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:56.898276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:58.778884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:00.653200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:02.638267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:04.531292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:06.705496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:08.648389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:10.656759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:12.828483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:14.767838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:16.894646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:49.481728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:51.253834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:53.188023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:55.260624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:57.020710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:58.899048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:00.810287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:02.753252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:04.685894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:06.835634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:08.972384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:10.780901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:12.957016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:14.893708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:17.018583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:49.588629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:51.349297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:53.306124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:55.365467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:57.140685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:59.011027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:00.935008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:02.874676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:04.820783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:06.965242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:09.088405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:10.903549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:13.087604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:15.018086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:17.138856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:49.701325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:51.448412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:53.423851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:55.463059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:57.254087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:59.124715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:01.044877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:02.985593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:04.937051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:07.097620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:09.202249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:11.035028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:13.221436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:15.147801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:17.265038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:49.816375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:51.569896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:53.570553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:55.714464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:57.370752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:59.244427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:01.162623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:03.122154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:05.068603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:07.225817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:09.322960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:11.160787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:13.347994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:15.269691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:17.395505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:49.924683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:51.687075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:53.728644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:55.820986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:57.492777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:59.363011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:01.281824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:03.250580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:05.205855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:07.355978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:09.439182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:11.285037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:13.485187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:15.391313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:17.517278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:50.056077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:51.789343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:53.893854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:55.927211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:57.605112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:59.478444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:01.394509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:03.356961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:05.554806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:07.483844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:09.564246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:11.411950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:13.618361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:15.687985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:17.639526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:50.172282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:51.910293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:54.039812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:56.049114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:57.733830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:59.597695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:01.510417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:03.484222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:05.684468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:07.615609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:09.698229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:11.540610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:13.749307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:15.802281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:17.772167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:50.300965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:52.235809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:54.166163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:56.168286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:57.852416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:59.718363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:01.632478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:03.614613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:05.813810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:07.747754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:09.822269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:11.670142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:13.871698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:15.923858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:17.889636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:50.413170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:52.351003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:54.298565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:56.270532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:57.963869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:59.862392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:01.737764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:03.736186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:05.953981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:07.884968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:09.938018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:11.804591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:13.993801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:16.049404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:18.013560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:50.533324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:52.452237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:54.425686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:56.379520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:58.084848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:00.098906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:02.082478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:03.856586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:06.086473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:08.021256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:10.055207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:11.934535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:14.135090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:16.175529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:18.138562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:50.643780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:52.585886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:54.549793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:56.490097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:58.202854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:00.238513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:02.203615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:04.004916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:06.218289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:08.156036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:10.185730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:12.241216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:14.271686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:16.304688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:18.259399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:50.764708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:52.689923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:54.682627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:56.590213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:48:58.310223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:00.337084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:02.321078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:04.133734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:06.338210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:08.268192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:10.299197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:12.354519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:14.402418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-24T10:49:16.420960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-24T10:49:24.183601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-24T10:49:24.309635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-24T10:49:18.470370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-24T10:49:18.822115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

StateAccountlengthAreacodeInternationalplanVoicemailplanNumbervmailmessagesTotaldayminutesTotaldaycallsTotaldaychargeTotaleveminutesTotalevecallsTotalevechargeTotalnightminutesTotalnightcallsTotalnightchargeTotalintlminutesTotalintlcallsTotalintlchargeCustomerservicecallsChurn
0KS128415NoYes25265.111045.07197.49916.78244.79111.0110.032.710
1OH107415NoYes26161.612327.47195.510316.62254.410311.4513.733.710
2NJ137415NoNo0243.411441.38121.211010.3162.61047.3212.253.2900
3OH84408YesNo0299.47150.961.9885.26196.9898.866.671.7820
4OK75415YesNo0166.711328.34148.312212.61186.91218.4110.132.7330
5AL118510YesNo0223.49837.98220.610118.75203.91189.186.361.700
6MA121510NoYes24218.28837.09348.510829.62212.61189.577.572.0330
7MO147415YesNo0157.07926.69103.1948.76211.8969.537.161.9200
8LA117408NoNo0184.59731.37351.68029.89215.8909.718.742.3510
9WV141415YesYes37258.68443.96222.011118.87326.49714.6911.253.0200

Last rows

StateAccountlengthAreacodeInternationalplanVoicemailplanNumbervmailmessagesTotaldayminutesTotaldaycallsTotaldaychargeTotaleveminutesTotalevecallsTotalevechargeTotalnightminutesTotalnightcallsTotalnightchargeTotalintlminutesTotalintlcallsTotalintlchargeCustomerservicecallsChurn
3323IN117415NoNo0118.412620.13249.39721.19227.05610.2213.633.6751
3324WV159415NoNo0169.811428.87197.710516.8193.7828.7211.643.1310
3325OH78408NoNo0193.49932.88116.9889.94243.310910.959.342.5120
3326OH96415NoNo0106.612818.12284.88724.21178.9928.0514.974.0210
3327SC79415NoNo0134.79822.9189.76816.12221.41289.9611.853.1920
3328AZ192415NoYes36156.27726.55215.512618.32279.18312.569.962.6720
3329WV68415NoNo0231.15739.29153.45513.04191.31238.619.642.5930
3330RI28510NoNo0180.810930.74288.85824.55191.9918.6414.163.8120
3331CT184510YesNo0213.810536.35159.68413.57139.21376.265.0101.3520
3332TN74415NoYes25234.411339.85265.98222.6241.47710.8613.743.700